Instructions to use Phind/Phind-CodeLlama-34B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Phind/Phind-CodeLlama-34B-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Phind/Phind-CodeLlama-34B-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Phind/Phind-CodeLlama-34B-v2")
model = AutoModelForCausalLM.from_pretrained("Phind/Phind-CodeLlama-34B-v2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Phind/Phind-CodeLlama-34B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Phind/Phind-CodeLlama-34B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phind/Phind-CodeLlama-34B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Phind/Phind-CodeLlama-34B-v2

SGLang

How to use Phind/Phind-CodeLlama-34B-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Phind/Phind-CodeLlama-34B-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phind/Phind-CodeLlama-34B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Phind/Phind-CodeLlama-34B-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phind/Phind-CodeLlama-34B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Phind/Phind-CodeLlama-34B-v2 with Docker Model Runner:
```
docker model run hf.co/Phind/Phind-CodeLlama-34B-v2
```

I have some proof

by bhuwansaik - opened Aug 28, 2023

Discussion

bhuwansaik

Aug 28, 2023

Guys please have some decency - you gave me some lame reason last time and now you took down the old repo, create a new repo with changed config. This goes completely against OpenSource, have some shame.

michaelroyzen

Phind org Aug 28, 2023

•

edited Aug 28, 2023

Proof of what exactly? It's pretty obvious we didn't use their model. Our v1 model (released before Wizardcoder btw) was trained on a Wizardcoder-style dataset that we made ourselves and this was the internal nomenclature for the model.

Even based on this screenshot it wouldn't make any sense that we used their model because they have nothing about checkpoint information.

Again, we did not use anything from Wizardcoder and I want to make sure that we are extremely clear about this. It's obvious if you use our model that it is completely different -- theirs is derived from CodeLlama-34B-Python while this model is derived from CodeLlama-34B.

michaelroyzen changed discussion status to closed Aug 28, 2023

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